M. Mathea

Deep Learning Foundation Models for Low-Data Regimes from Classical Molecular Descriptors.

This work proposes pretraining on low-noise, calculable molecular descriptors via supervised learning to obtain rich, highly transferable molecular representations and demonstrates this strategy with CheMeleon, a O(10M) parameter foundation model that enables directed message-passing neural networks to finally exceed t...